Transcript of "Analisis Data"

1.
ANALISIS DATA
SARTIKA SARI
12/12/2013

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PURPOSE OF
ANALYSING THE DATA
- Learn the problem
- Find out the cause and the effect of the
phenomena
- Predict real phenomena based on research
- Find out answer of various problem
- Draw conclusion based on the problem
BASIC ELEMENTS IN ANALYSING THE DATA
- What (data/information)
- Who/where/how/what happen
(Scientific reasoning/argument)
- What result (Finding)
- So what/so how/therefore (Lesson/conclusion)
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4.
CHOOSE BASED ON CHARACTERISTICS OF
THE DATA
QUALITATIVE
QUANTITATIVE
EXAMPLES
- Quality of life of the local
community in Ubud
- Comparative analysis of
students’ achievement
between girls and boys in
- Local perception of tourism as
tourism institute
an indicator of destination
decline
- The effect of increase fuel
price towards local tourist
arrival
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6.
QUALITATIVE
Miles and Huberman (1994), analysis of qualitative data is NOT
sequential steps but happen at the same time plus over and over again.
Data collection
Data display
Data reduction
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Drawing /
verifying
conclusion

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A process of ...
• Data collection  collecting & gathering the data
in a form of a list  easier to be read and
analyzed
• Data reduction  transforming, selecting,
adding or reducing based on the needs
• Data display  classifying, categorizing, put the
data in which share certain similarities
• Concluding  verifying & formulating the
conclusion that can answer the phenomena
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8.
QUALITATIVE
Model by James P. Spradley
Pengamatan deskriptif
Pengamatan terfokus
Pengamatan terpilih
Component analysis
Taxonomy analysis
Domain analysis
Beginning of the research
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End of the research
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domain  TAXONOMY  component  ...
• Deeper analysis on certain domain based on the
needs/research focus
• Only use domains which have relationship with the
research being analysed
• Organizing elements with sharing the similarity in a
domain
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17.
Additional info for qualitative
data analysis
• New research  no literature study to compare  how to check the
validity of the data?
• Since some say that the foundation of qualitative are words
structured...to avoid this misconception, use triangulation!
• Findings of a study are true and certain—“true” means accurately
reflect the situation, and “certain” means supported by evidence.
1. Data triangulation (using variety of data source)
2. Investigator triangulation (using several investigator/team)
3. Theory triangulation (using multiple theory from different
discipline to interpretate single data)
4. Methodological triangulation (using multiple method to study a
single problem,e.g. FGD, survey, interview)
(Denzin, 1978)
5. Environmental triangulation (using different location, setting,
others related to environment); as long as the finding remain the
same although it’s influenced by environment factor  validity is
established.
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DESCRIPTIVE STATISTICS
• Analyzing data by describing the collected data with no
means to generalize
• Data are gathered from population
• In such case, it can be gathered from sample, but please
NOTE that the result cannot represent the population
• Example:
• Of 350 randomly students in SPB, 280 students had
choosen food production course. An example of
descriptive statistics is the following statement : "80% of
these students had choosen food production course."
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INFERENTIAL STATISTICS
• Analyzing data by using information from a sample to
infer something about a population
• The result can be used to generalize
• Example:
• Of 350 randomly students in SPB, 280 people had
choosen food production course. An example of
inferential statistics is the following statement : "80% of
SPB students had choosen food production course."
• The easiest way to tell that this statement is not
descriptive is by trying to verify it based upon the
information provided and or hypothesis testing
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22.
INFERENTIAL STATISTICS
• a result is considered significant not because it is
important or meaningful, but because it has been
predicted as unlikely to have occurred by chance alone.
• Level of significance is usually at 0.05 (5%)
• be less than 0.05, then the result would be considered
statistically significant and the null hypothesis would be
rejected.
• Example: ...
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23.
INFERENTIAL STATISTICS
• Example: ...level of significance
•
•
•
•
probably no difference between city and the suburbs, the probability is .795
(1 - 0.795 = 0.205) only a 20.5% chance that the difference is true.
In contrast the high significance level for type of vehicle 0.001
(1 – 0.001 = 0.999)  99.9% indicates there is almost certainly a true
difference in purchases of Brand X by owners of different vehicles in the
population from which the sample was drawn.
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